Adding examples with ImageSlider

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+hair.jpg filter=lfs diff=lfs merge=lfs -text
+maizi.jpg filter=lfs diff=lfs merge=lfs -text
+videoframe_10273.png filter=lfs diff=lfs merge=lfs -text
+videoframe_2168.png filter=lfs diff=lfs merge=lfs -text
+videoframe_3289.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -4,7 +4,7 @@ emoji: 💻
 colorFrom: blue
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.19.0
+sdk_version: 4.44.1
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -9,6 +9,7 @@ from torchvision.transforms import Compose
 import cv2
 from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file
+from gradio_imageslider import ImageSlider
 import spaces
 
 # Helper function to load model from Hugging Face
@@ -69,7 +70,7 @@ def process_image(image, model, device):
     
     # Convert to a PIL image
     depth_image = Image.fromarray(depth_colored_hwc)
-    return depth_image
+    return image, depth_image
 
 # Gradio interface function with GPU support
 @spaces.GPU
@@ -114,10 +115,11 @@ def gradio_interface(image):
 iface = gr.Interface(
     fn=gradio_interface,
     inputs=gr.Image(type="pil"),  # Only image input, no mode selection
-    outputs=gr.Image(type="pil"),  # Only depth image output, no debug info
+    outputs = ImageSlider(label="Depth slider", type="pil", slider_color="pink"), # Depth image out with a slider
     title="Depth Estimation Demo",
-    description="Upload an image to see the depth estimation results. Our model is running on GPU for faster processing."
-)
+    description="Upload an image to see the depth estimation results. Our model is running on GPU for faster processing.",
+    examples=["maizi.jpg", "hair.jpg", "videoframe_10273.png", "videoframe_2168.png", "videoframe_3289.png"],
+    cache_examples=True,)
 
 # Launch the Gradio interface
 iface.launch()